More data, on the cloud you already run.
Large cloud users are constrained by power, interconnect, and floor space. A .qzip file is compressed data those clouds can store and move, so the infrastructure already built carries more.
You cannot pour concrete fast enough.
New capacity arrives on a multi-year schedule and is gated by grid connections nobody controls. Meanwhile the bytes keep growing: bigger datasets, bigger checkpoints, more traffic between regions that were never meant to be one machine.
Interconnect is the bottleneck
Training at scale is increasingly limited by what moves between nodes and sites, not by what any single node can compute.
Datasets are written once and read forever
That is precisely the shape the engine is built for: spend the compute at encode, keep every read cheap.
Every byte carries an energy cost
Storage, transfer and the cooling that follows them all scale with volume. Fewer bytes is less of all three.
Where a .qzip file fits
- 01deployable nowTraining corpora
Written once, read across many runs and many clusters.
- 02deployable nowCheckpoints
Written constantly, read on restart and for evaluation. Pure encode-once traffic.
- 03deployable nowCross-site transfer
Moving datasets and weights between regions, billed by the terabyte.
- 04standards-boundLive inference path
Latency-critical serving, where decode budget is measured in microseconds.
Highlighted stages are inside infrastructure you operate, where a decoder is a library you link rather than a standard you wait for.
What changes
- 01More effective capacityThe same disks and the same links carry more, without a procurement cycle.
- 02Cheaper movement between clustersCross-region and cross-site transfer is a line item that scales directly with volume.
- 03Headroom bought with compute, not concreteEncode compute is elastic and schedulable. Building a data center is neither.
What we have measured, and what we have not.
The public result is on text data. We have not published figures for training corpora, checkpoints or any other AI workload, because we have not measured them under a protocol we would be willing to defend. If your data is the interesting case, the honest answer is to run it: your corpus, your tuned baseline, decompressor counted.